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Artificial Intelligence in Industrial Markets

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Artificial Intelligence in Industrial Markets ARTIFICIAL INTELLIGENCE IN INDUSTRIAL MARKETS , the Compass icon and the 3DS logo, CATIA, SOLIDWORKS, ENOVIA, DELMIA, SIMULIA, GEOVIA, EXALEAD, 3D VIA, 3DSWYM, BIOVIA, NETVIBES, and 3DEXCITE are commercial trademarks trademarks commercial are 3D VIA, 3DSWYM, BIOVIA, NETVIBES, and 3DEXCITE EXALEAD, SOLIDWORKS, ENOVIA, DELMIA, SIMULIA, GEOVIA, CATIA, and the 3DS logo, icon , the Compass EXPERIENCE® 3D Our 3DEXPERIENCE® platform powers our brand applications, serving 12 industries, and provides a rich portfolio of industry solution experiences. Dassault Systèmes, the 3DEXPERIENCE® Company, provides business and people with virtual universes to imagine sustainable innovations. Its world-leading solutions transform the way products are designed, produced, and supported. Dassault Systèmes’ collaborative solutions foster social innovation, expanding possibilities for the virtual world to improve the real world. The group brings value to over 230,000 customers of all sizes in all industries in more than 140 countries. For more information, visit www.3ds.com. ©2014 Dassault Systèmes. All rights reserved. Systèmes. ©2014 Dassault approval. written their express is subject to or its subsidiaries Systèmes trademarks owners. Use of any Dassault owned by their respective are All other trademarks other countries. or its subsidiaries Systèmes in the U.S. of Dassault and/or trademarks or registered Americas Europe/Middle East/Africa Asia-Pacific Dassault Systèmes Dassault Systèmes Dassault Systèmes K.K. 175 Wyman Street 10, rue Marcel Dassault ThinkPark Tower Waltham, Massachusetts CS 40501 2-1-1 Osaki, Shinagawa-ku, 02451-1223 78946 Vélizy-Villacoublay Cedex Tokyo 141-6020 USA France Japan WHY ARTIFICIAL INTELLIGENCE MATTERS TO INDUSTRIAL MARKETS Industries like manufacturing, mining and construction are sometimes characterized as digital Luddites, with an odd attachment to business relics like fax machines, paper catalogs, clipboards and Post-it Notes. US$690B In reality, of course, industrial companies have been at the forefront of technological innovation Approximate amount IDC for decades, with sophisticated robots and other complex, highly-automated machinery widely forecasts discrete and process deployed on the shop floor and in the field. manufacturing will spend on digital transformation solutions Nonetheless, it is true that there is a real, widely-acknowledged gap between this operational in 2023, representing nearly technology and business-centered information technology (IT). Industrial markets have long 30% of total global trailed others in their level of IT investment. This, however, is about to change. DX spending. Industrial companies are now expected to outspend their B2C counterparts on digital IDC Worldwide Semiannual transformation solutions. Will this investment yield the same kind of disruption and Digital Transformation transformation seen in industries like retail, banking, insurance and health care? This remains Spending Guide to be seen. Digital Transformation is More Than Simple Digitalization Global market intelligence firm IDC has found that most manufacturers’ digital transformation investments will not yield the results they seek. Why? For some, it will be because their efforts are not really geared toward “digital transformation” but rather “digitalization”, which is focused primarily on improving efficiency. While efficiencies are desirable (who doesn’t want to save time, lower costs, or reduce waste?), the changes resulting from digitalization are not transformational, and the yields are likely to be minimal: industrial companies have already spent years using dozens of strategies to squeeze out every ounce of production and supply chain efficiency they can. Digital transformation, on the other hand, goes far beyond efficiency. It is a strategy that encompasses digitalization, yes; but it also enables continuous and substantive process improvement, increased agility and, most importantly, breakthrough innovation. What is Digital Transformation? IT analyst firm Gartner, Inc., defines “digital business transformation” as “the process of exploiting digital technologies and supporting capabilities to create a robust new digital business model.” It’s an important definition in that it places “business” literally in the middle of “digital transformation,” and it foregrounds the development of a new business model that is data-centered (“a new digital business model”). It is the kind of definition Jeffrey Immelt, former Chairman and CEO of General Electric, had in mind as he mused on the impact of the “If you went to bed last night as an industrial Internet of Things (IoT) on industrial companies. His advice to his company, you’re going to wake up today as a peers? Digital transformation is going to reshape your market more software and analytics company.” profoundly and more rapidly than you can imagine: “If you went Jeffrey Immelt, former Chairman & CEO to bed last night as an industrial company, you’re going to wake up General Electric today as a software and analytics company.” Specifically, digital transformation involves using data, analytics, and connectivity to rethink everything you do from the perspective of your customer, and using these same tools as the backbone of new products and services for a compelling, personalized customer experience. Let‘s see how this kind of deep transformation can be achieved via advanced technologies like artificial intelligence and machine learning. 2 Artificial Intelligence in Industrial Markets INDUSTRIAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE & MACHINE LEARNING The use cases for artificial intelligence (AI) and machine learning (ML) in industrial markets (see Appendix) are quite broad and diverse, but behaviorally they are somewhat similar. Like a valued butler, a good AI or ML application is a helpmate, anticipating needs, managing tasks, and providing trusted advice (recommendations). Below are some examples of the kind of valuable assistance AI and ML can provide throughout a product’s or an asset’s lifecycle. Predictive Maintenance and Field Operations The most commonly cited industrial application of AI is predictive maintenance, e.g. the ability to predict when an equipment failure will happen to avoid expensive downtime costs. Few people realize how much AI can be used upstream, downstream, and beyond these specific AI-powered predictive models in order to design and scale the benefits of AI to all field operations. ML-enabled field support can assist with: • Maintenance, Repair and Operations planning; • Generation of preventive and predictive maintenance recommendations; • Analysis of quality issues; • Automation of routine operations and maintenance tasks using automation software, robots, autonomous vehicles and drones; • Interpreting and funneling operational data back to teams working on service design; and • Interpreting and sharing performance data, along with other quality-related data like customer feedback and warranty information, to manufacturing teams. BENEFITS OF PREVENTIVE MAINTENANCE Improved reliability and life of equipment Fewer costly repairs and downtimes associated with unexpected equipment failure Fewer errors in operations as a result of equipment working incorrectly Reduced health and safety risks Design In the conceptual phase of product development, ML is applied in combination with virtual engineering models and simulation for iterative design. With these technologies, millions of design options can be cycled through in an instant, with recommendations automatically generated for optimal solutions based on multiple criteria (cost, sustainability, time, regulatory requirements, etc.). AI and ML are also valuable in the earliest ideation stages. They are being used in cognitive search systems to help designers explore existing design concepts via both text and image searches. And they can help designers understand customer demand through analysis of sources like social media or internal customer feedback systems. Artificial Intelligence in Industrial Markets 3 Testing ML can also be used to develop highly accurate digital models of both physical objects and systems. This enables the development of realistic behavioral models that can be used to run performance simulations. This use case is so well-established that physical prototyping has been all but eliminated in some industries (architecture, automotive, aerospace). Manufacturing ML-powered digital modeling and simulation (including virtual reality systems) are also being used to 1) plan production lines and systems, 2) develop and integrate smart equipment, smart robots and production-line drones, 3) recommend and execute proactive maintenance (preventive/predictive maintenance), and 4) funnel important production data back to teams working on product design and specifications. Factory Flow Simulation Sales & Marketing In commercialization phases, AI and ML applications are being used to: • Predict demand trends, • Deliver highly personalized/micro-targeted marketing, • Create intelligent, multi-lingual bot assistants for self-service ordering and support, • Power sales- and marketing-related virtual and augmented reality applications, and • Customize products and services. Drones are being integrated into logistics systems A 3D plant model can be used for training 4 Artificial Intelligence in Industrial Markets Summary Benefits of Machine Learning in Industrial Contexts The most significant payoffs of using ML in industrial sectors include greater innovation, process optimization
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